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The Scary Black Box: AI Driven Recommender Algorithms as The Most Powerful Social Force

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2022
Etnoantropoloski+problemi+2022-02+11+Bojic-Bulatovic-Zikic.pdf (353.3Kb)
Authors
Bojić, Ljubiša
Bulatović, Aleksandra
Žikić, Simona
Article (Published version)
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Abstract
Recommender algorithms shape societies by individually exposing online users to everything they see, hear and feel online in real time. We examine development of recommender algorithms from the Page Rank and advertising platforms to Social Media Trending tools to draw conclusions about their social effects. Decisions on how to simplify the complex world around us into dozens of possibilities immensely affect societies and individuals. Similar to our perceptive apparatus, algorithms are eyes and ears in the online world, as they focus our attention towards what they "think" should be important, which is similar to news priming. That's why recommender algorithms are compared to mass media given their similar roles to sell products and prolong content exposure of online users. This inquiry concludes that AI driven recommender algorithms represent the most powerful social force at present. This indicates that recommender algorithms should be transparent to everyone and controlled by societ...y as a public good. As recommender algorithms are usually based on artificial intelligence, human beings cannot see what's inside the black box, but should be able to set them for the benefit of individual and social well being. The fact that algorithms can be customized empowers societies to tackle the issues such as fake news, social polarization, echo chambers and spread of negative emotions, which ultimately affect individual well being and democratic capacity. Limitation of this inquiry is lack of quantitative analyisis. The main recommendations for further research is experiment on how much algorithms can predict our needs and wants.

Keywords:
Recommender systems / mass media / social polarization / echo chambers / negative news
Source:
Etnoantropološki problemi, 2022, 17, 2, 719-744
Publisher:
  • Faculty of Philosophy, University of Belgrade – Department of Ethnology and Anthropology.
Funding / projects:
  • Ministry of Education, Science and Technological Development, Republic of Serbia, Grant no. 200025 (University of Belgrade, Institute for Phylosophy and Social Theory) (RS-200025)

DOI: 10.21301/eap.v17i2.11

Cobiss ID: 29922138

ISBN: 0353-1589

[ Google Scholar ]
URI
http://rifdt.instifdt.bg.ac.rs/123456789/2687
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  • Radovi istraživača
Institution/Community
IFDT
TY  - JOUR
AU  - Bojić, Ljubiša
AU  - Bulatović, Aleksandra
AU  - Žikić, Simona
PY  - 2022
UR  - http://rifdt.instifdt.bg.ac.rs/123456789/2687
AB  - Recommender algorithms shape societies by individually exposing online users to everything they see, hear and feel online in real time. We examine development of recommender algorithms from the Page Rank and advertising platforms to Social Media Trending tools to draw conclusions about their social effects. Decisions on how to simplify the complex world around us into dozens of possibilities immensely affect societies and individuals. Similar to our perceptive apparatus, algorithms are eyes and ears in the online world, as they focus our attention towards what they "think" should be important, which is similar to news priming. That's why recommender algorithms are compared to mass media given their similar roles to sell products and prolong content exposure of online users. This inquiry concludes that AI driven recommender algorithms represent the most powerful social force at present. This indicates that recommender algorithms should be transparent to everyone and controlled by society as a public good. As recommender algorithms are usually based on artificial intelligence, human beings cannot see what's inside the black box, but should be able to set them for the benefit of individual and social well being. The fact that algorithms can be customized empowers societies to tackle the issues such as fake news, social polarization, echo chambers and spread of negative emotions, which ultimately affect individual well being and democratic capacity. Limitation of this inquiry is lack of quantitative analyisis. The main recommendations for further research is experiment on how much algorithms can predict our needs and wants.
PB  - Faculty of Philosophy, University of Belgrade – Department of Ethnology and Anthropology.
T2  - Etnoantropološki problemi
T1  - The Scary Black Box: AI Driven Recommender Algorithms as The Most Powerful Social Force
IS  - 2
VL  - 17
SP  - 719
EP  - 744
DO  - 10.21301/eap.v17i2.11
ER  - 
@article{
author = "Bojić, Ljubiša and Bulatović, Aleksandra and Žikić, Simona",
year = "2022",
abstract = "Recommender algorithms shape societies by individually exposing online users to everything they see, hear and feel online in real time. We examine development of recommender algorithms from the Page Rank and advertising platforms to Social Media Trending tools to draw conclusions about their social effects. Decisions on how to simplify the complex world around us into dozens of possibilities immensely affect societies and individuals. Similar to our perceptive apparatus, algorithms are eyes and ears in the online world, as they focus our attention towards what they "think" should be important, which is similar to news priming. That's why recommender algorithms are compared to mass media given their similar roles to sell products and prolong content exposure of online users. This inquiry concludes that AI driven recommender algorithms represent the most powerful social force at present. This indicates that recommender algorithms should be transparent to everyone and controlled by society as a public good. As recommender algorithms are usually based on artificial intelligence, human beings cannot see what's inside the black box, but should be able to set them for the benefit of individual and social well being. The fact that algorithms can be customized empowers societies to tackle the issues such as fake news, social polarization, echo chambers and spread of negative emotions, which ultimately affect individual well being and democratic capacity. Limitation of this inquiry is lack of quantitative analyisis. The main recommendations for further research is experiment on how much algorithms can predict our needs and wants.",
publisher = "Faculty of Philosophy, University of Belgrade – Department of Ethnology and Anthropology.",
journal = "Etnoantropološki problemi",
title = "The Scary Black Box: AI Driven Recommender Algorithms as The Most Powerful Social Force",
number = "2",
volume = "17",
pages = "719-744",
doi = "10.21301/eap.v17i2.11"
}
Bojić, L., Bulatović, A.,& Žikić, S.. (2022). The Scary Black Box: AI Driven Recommender Algorithms as The Most Powerful Social Force. in Etnoantropološki problemi
Faculty of Philosophy, University of Belgrade – Department of Ethnology and Anthropology.., 17(2), 719-744.
https://doi.org/10.21301/eap.v17i2.11
Bojić L, Bulatović A, Žikić S. The Scary Black Box: AI Driven Recommender Algorithms as The Most Powerful Social Force. in Etnoantropološki problemi. 2022;17(2):719-744.
doi:10.21301/eap.v17i2.11 .
Bojić, Ljubiša, Bulatović, Aleksandra, Žikić, Simona, "The Scary Black Box: AI Driven Recommender Algorithms as The Most Powerful Social Force" in Etnoantropološki problemi, 17, no. 2 (2022):719-744,
https://doi.org/10.21301/eap.v17i2.11 . .

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